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Data Annotation Manager Jobs in California (NOW HIRING)

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Data Annotation Manager information

See California salary details

$30.6K

$95.9K

$169.7K

How much do data annotation manager jobs pay per year?

As of Sep 1, 2026, the average yearly pay for data annotation manager in California is $95,873.00, according to ZipRecruiter salary data. Most workers in this role earn between $65,100.00 and $123,900.00 per year, depending on experience, location, and employer.

What does a data annotation manager do?

A Data Annotation Manager oversees the process of labeling and categorizing data used to train machine learning models. They manage teams of annotators, ensure data quality, develop annotation guidelines, and coordinate with data scientists to meet project requirements. Their role is critical in maintaining high standards of accuracy and efficiency, as well as ensuring that datasets are properly prepared for AI and machine learning applications.

What are the key skills and qualifications needed to thrive as a data annotation manager?

To thrive as a Data Annotation Manager, you need expertise in data labeling processes, quality control, and a solid understanding of machine learning concepts, usually backed by a degree in computer science or a related field. Proficiency with annotation tools such as Labelbox, Supervisely, or CVAT, as well as experience with project management systems, is commonly required. Exceptional leadership, attention to detail, and strong communication skills help manage teams and ensure high annotation accuracy. These skills are critical for delivering reliable labeled datasets, which are essential for building effective AI and machine learning models.

What are some common challenges faced by data annotation managers, and how can they be addressed?

Data Annotation Managers often encounter challenges such as maintaining high annotation quality across large and diverse datasets, managing a distributed team of annotators, and meeting tight project deadlines. To address these, it's important to implement robust quality assurance processes, provide ongoing training for annotators, and establish clear communication channels. Leveraging annotation tools with built-in validation features can also help ensure consistency and accuracy. Building a positive and collaborative team environment further contributes to better outcomes and workflow efficiency.

What is the difference between Data Annotation Manager vs Data Labeling Specialist?

AspectData Annotation ManagerData Labeling Specialist
CredentialsBachelor's degree in related field, experience in data managementHigh school diploma or equivalent, training in labeling tools
Work EnvironmentTeam management, project oversight, collaboration with data scientistsHands-on labeling work, using annotation tools, focused on data tagging
Industry UsageUsed in AI/ML projects for overseeing annotation teamsPerforms the actual data labeling tasks in machine learning workflows

The Data Annotation Manager oversees the entire annotation process, managing teams and ensuring quality, while the Data Labeling Specialist focuses on executing labeling tasks. Both roles are essential in AI/ML data preparation but differ in responsibilities and scope.

What are the most commonly searched types of Data Annotation jobs in California?

The most popular types of Data Annotation jobs in California are:

What are popular job titles related to Data Annotation Manager jobs in California?

For Data Annotation Manager jobs in California, the most frequently searched job titles are:

What job categories do people searching Data Annotation Manager jobs in California look for?

The top searched job categories for Data Annotation Manager jobs in California are:

What cities in California are hiring for Data Annotation Manager jobs?

Cities in California with the most Data Annotation Manager job openings:

Data Annotation Manager jobs near you
Infographic showing various Data Annotation Manager job openings in California as of August 2026, with employment types broken down into 85% Full Time, 14% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $95,873 per year, or $46.1 per hour.

Operations & Data Annotation Specialist

Avanciers Inc.

Cupertino, CA • On-site

Other

Posted 12 days ago

Job description

Avanciers is a premier IT Staffing/Consulting organization and we are currently recruiting for a Contract role for one of our premier client in USA for Operations & Data Annotation Specialist


Role: Operations & Data Annotation Specialist

Location: Los Angeles, CA / San Diego, CA / Cupertino, CA


Position Summary

The Operations & Data Annotation Specialist will support data annotation and validation workflows by assisting with QA audits, annotation tool configuration, task tracking, test-plan execution, and operational reporting. The role requires strong attention to detail, process compliance, and coordination with cross-functional teams.

Key Responsibilities

  • Support data annotation and validation operations according to defined processes and guidelines.
  • Perform QA audits and review annotation quality for accuracy and consistency.
  • Configure and maintain annotation tools and task workflows.
  • Track annotation tasks, priorities, issues, and completion status.
  • Support test-plan execution and document results, defects, and observations.
  • Prepare operational and quality reports for project stakeholders.
  • Identify process gaps and escalate quality or operational issues.
  • Coordinate with data, QA, engineering, and operations teams to resolve workflow issues.
  • Maintain accurate documentation and ensure compliance with established procedures.

Required Qualifications

  • 2–3 years of experience in data annotation, QA operations, data operations, or a related field.
  • Experience working with annotation or data-labeling tools.
  • Understanding of QA audits, task tracking, and quality processes.
  • Experience supporting test plans and operational reporting.
  • Strong attention to detail and analytical skills.
  • Good communication and stakeholder coordination skills.
  • Ability to work onsite and manage multiple operational priorities.

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